SoroushVahidi/scidocs huggingface dataset:
Measuring emotional intelligence with the MSCEIT V2.0.
(2003)

Does a recently introduced ability scale adequately measure emotional intelligence (EI) skills? Using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; J. D. Mayer, P. Salovey, & D. R. Caruso, 2002b), the authors examined (a) whether members of a general standardization sample and emotions experts identified the same test answers as correct, (b) the test's reliability, and (c) the possible factor structures of EI. Twenty-one emotions experts endorsed many of the same answers, as did 2,112 members of the standardization sample, and exhibited superior agreement, particularly when research provides clearer answers to test questions (e.g., emotional perception in faces). The MSCEIT achieved reasonable reliability, and confirmatory factor analysis supported theoretical models of EI. These findings help clarify issues raised in earlier articles published in Emotion.
A complete formalized knowledge representation model for advanced digital forensics timeline analysis
(2014)

Having a clear view of events that occurred over time is a difficult objective to achieve in digital investigations (DI). Event reconstruction, which allows investigators to understand the timeline of a crime, is one of the most important step of a DI process. This complex task requires exploration of a large amount of events due to the pervasiveness of new technologies nowadays. Any evidence produced at the end of the investigative process must also meet the requirements of the courts, such as reproducibility, verifiability, validation, etc. For this purpose, we propose a new methodology, supported by theoretical concepts, that can assist investigators through the whole process including the construction and the interpretation of the events describing the case. The proposed approach is based on a model which integrates knowledge of experts from the fields of digital forensics and software development to allow a semantically rich representation of events related to the incident. The main purpose of this model is to allow the analysis of these events in an automatic and efficient way. This paper describes the approach and then focuses on the main conceptual and formal aspects: a formal incident modelization and operators for timeline reconstruction and analysis. © 2014 Digital Forensics Research Workshop. Published by Elsevier Limited. All rights
Inverse Reinforcement Learning Based Human Behavior Modeling for Goal Recognition in Dynamic Local Network Interdiction
(2018)

Goal recognition is the task of inferring an agent’s goals given some or all of the agent’s observed actions. Among different ways of problem formulation, goal recognition can be solved as a model-based planning problem using off-theshell planners. However, obtaining accurate cost or reward models of an agent and incorporating them into the planning model becomes an issue in real applications. Towards this end, we propose an Inverse Reinforcement Learning (IRL)based opponent behavior modeling method, and apply it in the goal recognition assisted Dynamic Local Network Interdiction (DLNI) problem. We first introduce the overall framework and the DLNI problem domain of our work. After that, an IRL-based human behavior modeling method and Markov Decision Process-based goal recognition are introduced. Experimental results indicate that our learned behavior model has a higher tracking accuracy and yields better interdiction outcomes than other models.
Author Disambiguation using Error-driven Machine Learning with a Ranking Loss Function
(2007)

Author disambiguation is the problem of determining whether records in a publications database refer to the same person. A common supervised machine learning approach is to build a classifier to predict whether a pair of records is coreferent, followed by a clustering step to enforce transitivity. However, this approach ignores powerful evidence obtainable by examining sets (rather than pairs) of records, such as the number of publications or co-authors an author has. In this paper we propose a representation that enables these first-order features over sets of records. We then propose a training algorithm well-suited to this representation that is (1) error-driven in that training examples are generated from incorrect predictions on the training data, and (2) rank-based in that the classifier induces a ranking over candidate predictions. We evaluate our algorithms on three author disambiguation datasets and demonstrate error reductions of up to 60% over the standard binary classification approach.
Closed-Loop Deep Brain Stimulation Is Superior in Ameliorating Parkinsonism
(2011)

Continuous high-frequency deep brain stimulation (DBS) is a widely used therapy for advanced Parkinson's disease (PD) management. However, the mechanisms underlying DBS effects remain enigmatic and are the subject of an ongoing debate. Here, we present and test a closed-loop stimulation strategy for PD in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) primate model of PD. Application of pallidal closed-loop stimulation leads to dissociation between changes in basal ganglia (BG) discharge rates and patterns, providing insights into PD pathophysiology. Furthermore, cortico-pallidal closed-loop stimulation has a significantly greater effect on akinesia and on cortical and pallidal discharge patterns than standard open-loop DBS and matched control stimulation paradigms. Thus, closed-loop DBS paradigms, by modulating pathological oscillatory activity rather than the discharge rate of the BG-cortical networks, may afford more effective management of advanced PD. Such strategies have the potential to be effective in additional brain disorders in which a pathological neuronal discharge pattern can be recognized.
ECG signal analysis and arrhythmia detection on IoT wearable medical devices
(2016)

Healthcare is one of the most rapidly expanding application areas of the Internet of Things (IoT) technology. IoT devices can be used to enable remote health monitoring of patients with chronic diseases such as cardiovascular diseases (CVD). In this paper we develop an algorithm for ECG analysis and classification for heartbeat diagnosis, and implement it on an IoT-based embedded platform. This algorithm is our proposal for a wearable ECG diagnosis device, suitable for 24-hour continuous monitoring of the patient. We use Discrete Wavelet Transform (DWT) for the ECG analysis, and a Support Vector Machine (SVM) classifier. The best classification accuracy achieved is 98.9%, for a feature vector of size 18, and 2493 support vectors. Different implementations of the algorithm on the Galileo board, help demonstrate that the computational cost is such, that the ECG analysis and classification can be performed in real-time.
Ubiquitous Data Accessing Method in IoT-Based Information System for Emergency Medical Services
(2014)

The rapid development of Internet of things (IoT) technology makes it possible for connecting various smart objects together through the Internet and providing more data interoperability methods for application purpose. Recent research shows more potential applications of IoT in information intensive industrial sectors such as healthcare services. However, the diversity of the objects in IoT causes the heterogeneity problem of the data format in IoT platform. Meanwhile, the use of IoT technology in applications has spurred the increase of real-time data, which makes the information storage and accessing more difficult and challenging. In this research, first a semantic data model is proposed to store and interpret IoT data. Then a resource-based data accessing method (UDA-IoT) is designed to acquire and process IoT data ubiquitously to improve the accessibility to IoT data resources. Finally, we present an IoT-based system for emergency medical services to demonstrate how to collect, integrate, and interoperate IoT data flexibly in order to provide support to emergency medical services. The result shows that the resource-based IoT data accessing method is effective in a distributed heterogeneous data environment for supporting data accessing timely and ubiquitously in a cloud and mobile computing platform.
The impact of the MIT-BIH Arrhythmia Database
(2001)

The MIT-BIH Arrhythmia Database was the first generally available set of standard test material for evaluation of arrhythmia detectors, and it has been used for that purpose as well as for basic research into cardiac dynamics at about 500 sites worldwide since 1980. It has lived a far longer life than any of its creators ever expected. Together with the American Heart Association Database, it played an interesting role in stimulating manufacturers of arrhythmia analyzers to compete on the basis of objectively measurable performance, and much of the current appreciation of the value of common databases, both for basic research and for medical device development and evaluation, can be attributed to this experience. In this article, we briefly review the history of the database, describe its contents, discuss what we have learned about database design and construction, and take a look at some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database.
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
(2000)

The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of Health, is intended to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. The resource has 3 interdependent components. PhysioBank is a large and growing archive of well-characterized digital recordings of physiological signals and related data for use by the biomedical research community. It currently includes databases of multiparameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and from patients with a variety of conditions with major public health implications, including life-threatening arrhythmias, congestive heart failure, sleep apnea, neurological disorders, and aging. PhysioToolkit is a library of open-source software for physiological signal processing and analysis, the detection of physiologically significant events using both classic techniques and novel methods based on statistical physics and nonlinear dynamics, the interactive display and characterization of signals, the creation of new databases, the simulation of physiological and other signals, the quantitative evaluation and comparison of analysis methods, and the analysis of nonstationary processes. PhysioNet is an on-line forum for the dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. It provides facilities for the cooperative analysis of data and the evaluation of proposed new algorithms. In addition to providing free electronic access to PhysioBank data and PhysioToolkit software via the World Wide Web (http://www.physionet. org), PhysioNet offers services and training via on-line tutorials to assist users with varying levels of expertise.
A One-Stage Correction of the Blepharophimosis Syndrome Using a Standard Combination of Surgical Techniques
(2011)

The aim of this study was to evaluate the efficacy of a one-stage treatment for the blepharophimosis-ptosis-epicanthus inversus syndrome (BPES) using a combination of standard surgical techniques. This is a retrospective interventional case series study of 21 BPES patients with a 1-year minimum follow-up period. The one-stage intervention combined three different surgical procedures in the following order: Z-epicanthoplasty for the epicanthus, transnasal wiring of the medial canthal ligaments for the telecanthus, and a bilateral fascia lata sling for ptosis correction. Preoperative and postoperative measurements of the horizontal lid fissure length (HFL), vertical lid fissure width (VFW), nasal intercanthal distance (ICD), and the ratio between the intercanthal distance and the horizontal fissure length (ICD/HFL) were analyzed using Student’s t test for paired variables. The mean preoperative measurements were 4.95 ± 1.13 mm for the VFW, 20.90 ± 2.14 mm for the HFL, 42.45 ± 2.19 mm for the ICD, and 2.04 ± 0.14 mm for the ICD/HFL ratio. The mean postoperative measurements were 7.93 ± 1.02 mm for the VFW, 26.36 ± 1.40 mm for the HFL, 32.07 ± 1.96 mm for the ICD, and 1.23 ± 0.09 mm for the ICD/HFL ratio. All these values and their differences were statistically significant (P < 0.0001). All of the patients developed symmetric postoperative inferior version lagophthalmus, a complication that tended to decrease over time. One-stage correction of BPES is safe and efficient with the surgical techniques described.
Time and cognitive load in working memory.
(2007)

According to the time-based resource-sharing model (P. Barrouillet, S. Bernardin, & V. Camos, 2004), the cognitive load a given task involves is a function of the proportion of time during which it captures attention, thus impeding other attention-demanding processes. Accordingly, the present study demonstrates that the disruptive effect on concurrent maintenance of memory retrievals and response selections increases with their duration. Moreover, the effect on recall performance of concurrent activities does not go beyond their duration insofar as the processes are attention demanding. Finally, these effects are not modality specific, as spatial processing was found to disrupt verbal maintenance. These results suggest a sequential and time-based function of working memory in which processing and storage rely on a single and general purpose attentional resource needed to run executive processes devoted to constructing, maintaining, and modifying ephemeral representations.
Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices
(2015)

Metric learning has been shown to be highly effective to improve the performance of nearest neighbor classification. In this paper, we address the problem of metric learning for symmetric positive definite (SPD) matrices such as covariance matrices, which arise in many real-world applications. Naively using standard Mahalanobis metric learning methods under the Euclidean geometry for SPD matrices is not appropriate, because the difference of SPD matrices can be a non-SPD matrix and thus the obtained solution can be uninterpretable. To cope with this problem, we propose to use a properly parameterized LogEuclidean distance and optimize the metric with respect to kernel-target alignment, which is a supervised criterion for kernel learning. Then the resulting non-trivial optimization problem is solved by utilizing the Riemannian geometry. Finally, we experimentally demonstrate the usefulness of our LogEuclidean metric learning algorithm on real-world classification tasks for EEG signals and texture patches.
Three Models for the Description of Language
(2006)

The grammar of a language is a device that describes the structure of that language. The grammar is comprised of a set of rules whose goal is twofold: first these rules can be used to create sentences of the associated language and only these, and second they can be used to classify whether a given sentence is an element of the language or not. The goal of a linguist is to discover grammars that are simple and yet are able to fully span the language. In [1] Chomsky describes three possible options of increasing complexity for English grammars: Finite-state, Phrase Structure and Transformational. This paper briefly present these three grammars and summarizes Chomsky’s analysis and results which state that finite-state grammars are inadequate because they fail to span all possible sentences of the English language, and phrase structure grammar is overly complex.
SemEval-2017 Task 7: Detection and Interpretation of English Puns
(2017)

Apun is a form of wordplay in which aword suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another word, for an intended humorous or rhetorical effect. Though a recurrent and expected feature in many discourse types, puns stymie traditional approaches to computational lexical semantics because they violate their one-sense-percontext assumption. This paper describes the first competitive evaluation for the automatic detection, location, and interpretation of puns. We describe the motivation for these tasks, the evaluation methods, and the manually annotated data set. Finally, we present an overview and discussion of the participating systems’ methodologies, resources, and results.
Stakeholder engagement, social auditing and corporate sustainability
(2006)

Purpose – To identify the applicability of social auditing as an approach of engaging stakeholders in assessing and reporting on corporate sustainability and its performance. Design/methodology/approach – Drawing upon the framework of AA1000 and the social auditing studies, this paper links stakeholder engagement, social auditing and corporate sustainability with a view to applying dialogue-based social auditing to address corporate sustainability. Findings – This paper identifies a “match” between corporate sustainability and social auditing, as both aim at improving the social, environmental and economic performance of an organisation, considering the well-being of a wider range of stakeholders and requiring the engagement of stakeholders in the process. This paper suggests that social auditing through engaging stakeholders via dialogue could be applied to build trusts, identify commitment and promote co-operation amongst stakeholders and corporations. Research limitations/implications – This research requires further empirical research into the practicality of social auditing in addressing corporate sustainability and the determination of the limitations of dialogue-based social auditing. Practical implications – Social auditing has been identified as a useful mechanism of balancing differing interests among stakeholders and corporations in a democratic business society. The application of social auditing in developing and achieving corporate sustainability has apparently practical implications. Originality/value – This paper examines the applicability of dialogue-based social auditing in helping business to move towards sustainability. Social auditing as a process of assessing and reporting on corporate social and environmental performance through engaging stakeholders via dialogue could be applied to build trusts, identify commitment and promote cooperation amongst stakeholders and corporations.
Using Semantic Web Technologies for Exploratory OLAP: A Survey
(2015)

This paper describes the convergence of some of the most influential technologies in the last few years, namely data warehousing (DW), on-line analytical processing (OLAP), and the Semantic Web (SW). OLAP is used by enterprises to derive important business-critical knowledge from data inside the company. However, the most interesting OLAP queries can no longer be answered on internal data alone, external data must also be discovered (most often on the web), acquired, integrated, and (analytically) queried, resulting in a new type of OLAP, exploratory OLAP. When using external data, an important issue is knowing the precise semantics of the data. Here, SW technologies come to the rescue, as they allow semantics (ranging from very simple to very complex) to be specified for web-available resources. SW technologies do not only support capturing the “passive” semantics, but also support active inference and reasoning on the data. The paper first presents a characterization of DW/OLAP environments, followed by an introduction to the relevant SW foundation concepts. Then, it describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms. Next, the paper goes on to survey the use of SW technologies for data modeling and data provisioning, including semantic data annotation and semantic-aware extract, transform, and load (ETL) processes. Finally, all the findings are discussed and a number of directions for future research are outlined, including SW support for intelligent MD querying, using SW technologies for providing context to data warehouses, and scalability issues.
Do cheerfulness , exhilaration , and humor production moderate pain tolerance ? A FACS study
(2017)

Prior studies have shown that watching a funny film leads to an increase in pain tolerance. The present study aimed at separating three factors considered potentially essential (mood, behavior, and cognition related to humor) and examined whether they are responsible for this effect. Furthermore, the study examined whether trait cheerfulness and trait seriousness, as measured by the State-TraitCheerfulness-Inventory (STCI; Ruch et al. 1996), moderate changes in pain tolerance. Fifty-sixty female subjects were assigned randomly to three groups, each having a different task to pursue while watching a funny film: (1) get into a cheerful mood without smiling or laughing (”Cheerfulness”); (2) smile and laugh extensively (”Exhilaration”); and (3) produce a humorous commentary to the film (”Humor production”). Pain tolerance was measured using the cold pressor test before, immediately after, and twenty minutes after the film. Results indicated that pain tolerance increased for participants from before to after watching the funny film and remained high for the twenty minutes. This effect was moderated by facial but not verbal indicators of enjoyment of humor. Participants low in trait seriousness had an overall higher pain tolerance. Subjects with a high score in trait cheerfulness showed an increase in pain tolerance after producing humor while watching the film whereas subjects low in trait cheerfulness showed a similar increase after smiling and laughter during the film. DOI: https://doi.org/10.1515/humr.2004.009 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-77579 Originally published at: Zweyer, K; Velker, B; Ruch, Willibald (2004). Do cheerfulness, exhilaration, and humor production moderate pain tolerance? A FACS study. HUMOR: International Journal of Humor Research, 17(12):85-119. DOI: https://doi.org/10.1515/humr.2004.009 Do cheerfulness, exhilaration, and humor production moderate pain tolerance? A FACS study KAREN ZWEYER, BARBARA VELKER, and WILLIBALD RUCH
A sneak peek into digital innovations and wearable sensors for cardiac monitoring
(2016)

Many mobile phone or tablet applications have been designed to control cardiovascular risk factors (obesity, smoking, sedentary lifestyle, diabetes and hypertension) or to optimize treatment adherence. Some have been shown to be useful but the long-term benefits remain to be demonstrated. Digital stethoscopes make easier the interpretation of abnormal heart sounds, and the development of pocket-sized echo machines may quickly and significantly expand the use of ultrasounds. Daily home monitoring of pulmonary artery pressures with wireless implantable sensors has been shown to be associated with a significant decrease in hospital readmissions for heart failure. There are more and more non-invasive, wireless, and wearable sensors designed to monitor heart rate, heart rate variability, respiratory rate, arterial oxygen saturation, and thoracic fluid content. They have the potential to change the way we monitor and treat patients with cardiovascular diseases in the hospital and beyond. Some may have the ability to improve quality of care, decrease the number of medical visits and hospitalization, and ultimately health care costs. Validation and outcome studies are needed to clarify, among the growing number of digital innovations and wearable sensors, which tools have real clinical value.